{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:JWKJWCUKIQMHCLUK6B5UOC5LYZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4173e3f36046f95dc7b66d8eda2784927a36dadc8fbed1fc3d1ef1c3d2212c6d","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-01T14:58:14Z","title_canon_sha256":"0a0ae5bc00f88c629b1075a8a20f27fd041992ef5ac9cc2f78844424260f2eed"},"schema_version":"1.0","source":{"id":"2503.00524","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.00524","created_at":"2026-07-05T10:22:24Z"},{"alias_kind":"arxiv_version","alias_value":"2503.00524v1","created_at":"2026-07-05T10:22:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.00524","created_at":"2026-07-05T10:22:24Z"},{"alias_kind":"pith_short_12","alias_value":"JWKJWCUKIQMH","created_at":"2026-07-05T10:22:24Z"},{"alias_kind":"pith_short_16","alias_value":"JWKJWCUKIQMHCLUK","created_at":"2026-07-05T10:22:24Z"},{"alias_kind":"pith_short_8","alias_value":"JWKJWCUK","created_at":"2026-07-05T10:22:24Z"}],"graph_snapshots":[{"event_id":"sha256:033e6bc2cd84ae653e69919bf0372ff1938346ed16a65f3d694b5b14cc3a79ca","target":"graph","created_at":"2026-07-05T10:22:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.00524/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models optimized via variational inference (VI) have emerged as a promising tool for generating samples from unnormalized target densities. These models create samples by simulating a stochastic differential equation, starting from a simple, tractable prior, typically a Gaussian distribution. However, when the support of this prior differs greatly from that of the target distribution, diffusion models often struggle to explore effectively or suffer from large discretization errors. Moreover, learning the prior distribution can lead to mode-collapse, exacerbated by the mode-seeking na","authors_text":"Denis Blessing, Gerhard Neumann, Xiaogang Jia","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-01T14:58:14Z","title":"End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.00524","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1f71c9116bf247a2cc54d4ab7e34467f217f9909efa0a7def840f42167c82b34","target":"record","created_at":"2026-07-05T10:22:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4173e3f36046f95dc7b66d8eda2784927a36dadc8fbed1fc3d1ef1c3d2212c6d","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-01T14:58:14Z","title_canon_sha256":"0a0ae5bc00f88c629b1075a8a20f27fd041992ef5ac9cc2f78844424260f2eed"},"schema_version":"1.0","source":{"id":"2503.00524","kind":"arxiv","version":1}},"canonical_sha256":"4d949b0a8a4418712e8af07b470babc65d8e324761ac35bea637d019b88fc1fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4d949b0a8a4418712e8af07b470babc65d8e324761ac35bea637d019b88fc1fd","first_computed_at":"2026-07-05T10:22:24.534504Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:22:24.534504Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5VQ0as3RosT+BeCLuU0zyNeB7uiX2zIIDnNhvWZmpzHyeiu2Jfw8X4dIQk/BrWQimc6Fp2d9pU2Kepp88RHmCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:22:24.535111Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.00524","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1f71c9116bf247a2cc54d4ab7e34467f217f9909efa0a7def840f42167c82b34","sha256:033e6bc2cd84ae653e69919bf0372ff1938346ed16a65f3d694b5b14cc3a79ca"],"state_sha256":"02265537f1c93df95915af694b47fa3b17bc37e635f480fd38fcdf1b44545575"}